Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa

Abstract

Background: Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than 20 years ago. Hence, these estimates are outdated due to large-scale preventive chemotherapy programs, improved sanitation, water resources development and management, among other reasons. For planning, coordination, and evaluation of control activities, it is essential to possess reliable schistosomiasis prevalence maps.

Methodology: We analyzed survey data compiled on a newly established open-access global neglected tropical diseases database (i) to create smooth empirical prevalence maps for Schistosoma mansoni and S. haematobium for individuals aged ≤ 20 years in West Africa, including Cameroon, and (ii) to derive country-specific prevalence estimates. We used Bayesian geostatistical models based on environmental predictors to take into account potential clustering due to common spatially structured exposures. Prediction at unobserved locations was facilitated by joint kriging.

Principal findings: Our models revealed that 50.8 million individuals aged ≤ 20 years in West Africa are infected with either S. mansoni, or S. haematobium, or both species concurrently. The country prevalence estimates ranged between 0.5% (The Gambia) and 37.1% (Liberia) for S. mansoni, and between 17.6% (The Gambia) and 51.6% (Sierra Leone) for S. haematobium. We observed that the combined prevalence for both schistosome species is two-fold lower in Gambia than previously reported, while we found an almost two-fold higher estimate for Liberia (58.3%) than reported before (30.0%). Our predictions are likely to overestimate overall country prevalence, since modeling was based on children and adolescents up to the age of 20 years who are at highest risk of infection.

Conclusion/significance: We present the first empirical estimates for S. mansoni and S. haematobium prevalence at high spatial resolution throughout West Africa. Our prediction maps allow prioritizing of interventions in a spatially explicit manner, and will be useful for monitoring and evaluation of schistosomiasis control programs.

Figure 1. Study profile.

Schematic overview of the study profiling process. The numbers in brackets in the acute-angled boxes represent the number of survey locations (which may not be unique) included in the current GNTD dataset, while the numbers outside the boxes represent the amount of survey dropped due to the reason given in the boxes with rounded corners.

Figure 2. Observed S. haematobium prevalence in West Africa.

Observed prevalence of S. haematobium among individuals aged ≤20 years across West Africa, including Cameroon.

Figure 3. Observed S. mansoni prevalence in West Africa.

Observed prevalence of S. mansoni among individuals aged ≤20 years across West Africa, including Cameroon.

Figure 4. Remotely sensed covariates.

Spatial distribution of remotely sensed covariates for West Africa, including Cameroon. Climatic covariates were summarized via yearly averages.

Figure 5. Predicted S. haematobium prevalence and standard deviation for West Africa.

(A) Predicted median of prevalence for S. haematobium among individuals aged ≤20 years during the period of 2000–2009 based on Bayesian kriging, and (B) standard deviation (SD) of the prediction error with sub-sampled survey locations.

Figure 6. Predicted S. mansoni prevalence and standard deviation for West Africa.

(A) Predicted median of prevalence for S. mansoni among individuals aged ≤20 years during the period of 2000–2009 based on Bayesian kriging, and (B) standard deviation (SD) of the prediction error with sub-sampled survey locations.